Field of Invention
The present invention relates generally to the field of simulation modeling and analysis. More specifically, the present invention is related to a system and method for design and execution of numerical experiments on a composite simulation model.
Discussion of Related Art
Decision-makers increasingly need to bring together multiple models across a broad range of disciplines to guide investment and policy decisions around highly complex issues such as population health and safety. Simulation-based optimization is a powerful and increasingly popular approach to the design and operation of highly complex systems over a wide variety of domains. For instance, the current list of test problems in the SimOpt.org library (see paper to R. Pasupathy et al. entitled “SIMOPT: A Library of Simulation Optimization Problems,” in Proc. Winter Simul. Conf, 2011, pp. 4080-4090) includes applications to vehicle routing, supply chains, healthcare facilities, fisheries management, finance, call centers, voting machines, air transportation networks, and more. Other recent application domains have included electrical grids (see paper to D. Phan et al. entitled “A Two-Stage Non-Linear Program for Optimal Electrical Grid Power Balance Under Uncertainty,” in Proc. Winter Simul. Conf, 2011, pp. 4227-4238) and environmental policymaking (see paper to Z. Hu et al. entitled “Robust Simulation of Environmental Policies Using the DICE Model,” in Proc. Winter Simul. Conf., 2010, pp. 1295-1305). Methodology for simulation optimization has developed along with applications; see, e.g., Chapters 17-21 in the book to S. G. Henderson et al. Eds., Simulation, ser. Handbooks in Operation Research and Management Science. Amsterdam, The Netherlands: Elsevier, 2006, vol. 13.
Currently, simulation optimization algorithms are typically applied to individual, domain-specific simulation models to solve relatively contained optimization problems. Simulation is increasingly being used, however, to guide investment and policy decisions around highly complex issues such as population health and safety (see publication by the Institute of Medicine entitled For the Public's Health: The Role of Measurement in Action and Accountability. The National Academies Press, 2010). In this setting, decision makers increasingly need to bring together multiple models across a broad range of disciplines. Such model composition is required to capture the behavior of complex “systems of systems” and gain synergistic understanding of highly complicated problems, avoiding unintended consequences of policy, investment, and operational decisions; see, e.g., the paper to H. Godfray et al. entitled “Linking Policy on Climate and Food,” Science, vol. 331, no. 6020, pp. 1013-1014, 2011, and the paper to T. T. Huang et al. entitled “A Systems-Oriented Multilevel Framework for Addressing Obesity in the 21st Century,” Preventing Chronic Disease, vol. 6, no. 3, 2009, in the setting of food, climate, and health. This composition task is extremely hard because domain experts have different worldviews, use different vocabularies, sit in different organizations, and have often invested considerable effort in developing and implementing their models using different programming paradigms and development platforms.
Such prior art systems, however, fail to address how such disparate simulation models may be combined, and what the implications would be for simulation-optimization methodology.